CryptoFlow Analytics

Crypto markets generate massive amounts of data across hundreds of exchanges, thousands of tokens, and multiple sentiment indicators. Individual investors and analysts face three core challenges: - Data fragmentation — Prices, volumes, sentiment, and trending data live in separate APIs with different formats - Signal noise — Raw price changes alone are misleading without context (volume confirmation, market breadth, sentiment) - Regime blindness — Most dashboards show what happened, but fail to classify where we are in the market cycle CryptoFlow Analytics solves this by building a unified intelligence layer that ingests, cleans, enriches, and analyzes crypto data to produce actionable signals - not just charts. :hammer_and_wrench: Bruin Features Used - Python Assets: 5 ingestion scripts fetching from CoinGecko, <http://Alternative.me|Alternative.me> APIs, and CSV seed - SQL Assets: 9 BigQuery SQL transformations across staging (3) and analytics (6) layers - Seed Assets: CSV-based reference data for coin categories - Materialization: table strategy for all assets; merge for incremental ingestion - Dependencies: Explicit depends declarations creating a proper DAG - Quality Checks: Built-in (not_null, unique, positive, accepted_values) on every asset - Custom Checks: Business logic validations (e.g., "Bitcoin must exist in data", "dominances sum to ~100%") - Glossary: Structured business term definitions for crypto concepts - Pipeline Schedule: Daily schedule via pipeline.yml - Bruin Cloud: Deployment, monitoring, and AI analyst - AI Data Analyst: Conversational analysis on all analytics tables - Lineage: Full column-level lineage via bruin lineage